Teddy Randby

*** * ******* ***, ****** Hill NC • 919-***-**** • adkems@r.postjobfree.com

EDUCATION

Bachelor of Arts, Computer Science Expected May 2021 University of North Carolina, Chapel Hill,Chapel Hill, NC SKILLS

● MongoDB, SQL, React, JQuery, Express, Node, GraphQL, REST, Bootstrap, Firebase, typeORM, Tensorflow, Google Cloud Services, Javascript, Typescript, Java, C++, HTML, CSS, Python, Haxe WORK HISTORY

Advanced Information Collaboratory (AIC) Research Fellow, University of Maryland June 2020 —Present

“Using AI and ML to Optimize Information Discovery in Under-utilized, Holocaust-related Records”, a collaboration with the FDR Presidential Library and Museum

● Develop text-extraction techniques using Google AutoML to accelerate processing of documents

● Utilize client-server architecture to enable quick iteration and experimentation

● Incorporate Tensorflow models into a deployed GraphQL api Fullstack Developer,University of North Carolina at Chapel Hill, Computer Science Department August 2017 — May 2018

● Collaborated with clients and other developers around the university on software projects

● Learned new codebases quickly in order to fix bugs and implement new features

● Collaborated with other developers to write maintainable and robust code PROJECT HISTORY

Frontend Developer and Architect, Avian Diet Database Interface August 2020 — Present

● Collaborate with team members to maintain a readable and structured code-base

● Develop test-first to encourage agile development

● Lead the development and design of a React application

● Manage complex state and GraphQL queries

Fullstack Developer, Ultimate Frisbee Tournament Organizer February 2020 — May 2020

● Built a responsive web app from the ground up using the MERN stack

● Practiced mobile-first development principles

● Practiced modular design to enable rapid iteration Fullstack Developer, Current Ultimate Frisbee Player Tracker January 2020 — February 2020

● Worked with a client to design a user-centric interface

● Implemented design using modern mobile-first development principles Fullstack Developer,Ultimate Frisbee Score Predictor May 2017 - August 2018

● Designed, built, and deployed a full stack web based application

● Integrated the front and backend with a documented GraphQL API OTHER EXPERIENCE

Club Athlete,UNC Darkside, Men’s Ultimate Frisbee August 2017 - Present

● Collaborate with 20+ athletes to facilitate a healthy team environment

● Develop focus and persistence to meet both personal and team goals AWARDS

● 2018 College Club Ultimate Frisbee Runners Up

● 2017 College Club Ultimate Frisbee National Champions R&D EXPERIENCE

Research Appointment:

Nominated in 2020 to serve as an AIC Research Fellow (Advanced Information Collaboratory (AIC). See: http://ai-collaboratory.net . The AIC is an international network dedicated to:

EXPLORING the opportunities and challenges of “disruptive technologies” for archives and records management.

PURSUING multidisciplinary collaborations to share relevant knowledge across domains.

LEVERAGING the latest technologies to unlock the hidden information in massive stores of records.

TRAINING current and future generations of information professionals to think computationally and rapidly adapt new technologies to meet their increasingly large and complex workloads.

PROMOTING ethical information access and use.

Presentations:

● Invited speaker at the CLIR / AERI2020 July 6, 2020 Webinar on “Emerging Technologies, Big Data & Archives”. Featuring 200 attendees from over 30 countries. See:

https://ai-collaboratory.net/2020/07/06/clir-aeri2020-webinar-on-emerging-technologies-big-data-archives/

● Research paper presentation at the “Computational Archival Science: digital records in the age of big data” workshop on Dec. 11, 2020: “Digital Curation and Machine Learning Experimentation in Archives”.

● Invited speaker at the 1st AIC International Research Network Symposium, Dec. 14, 2020. Talk on Machine Learning and Cultural Heritage collections.

Publications:

● “Digital Curation and Machine Learning Experimentation in Archives”, Teddy Randby and Richard Marciano, 5th Computational Archival (CAS) Workshop at the IEEE Big Data 2020 Conference. See: https://ai-collaboratory.net/wp-content/uploads/2020/11/Randby.pdf

● Accepted for publication in the Journal of AI & Society, Special issue on “Born Digital” – Shedding Light into the Darkness of Digital Culture, “Using AI and ML to Optimize Information Discovery in Under-utilized, Holocaust-related Records”, Kirsten Carter, Abby Gondek, Teddy Randby, Richard Marciano. Funded Research Projects:

● NATIONAL PARK SERVICE: Developing a Digital Asset Management System for the Archival Holdings of the Mary McLeod Bethune Council House National Historic Site A $519K National Park Service (NPS)-funded prototype study for a Digital Asset Management System (DAMS) to better preserve and manage the current and future digital assets of the Mary McLeod Bethune Council House National Historic Site. The archival holdings of the site represent the institutional holdings of the National Council for Negro Women and their efforts to create a National Archive for Black Women’s History. The organization was fundamental to the civil rights efforts of the twentieth century and the holdings are an invaluable resource documenting the accomplishments of black women throughout the nineteenth and twentieth century.

● INSTITUTE FOR MUSEUM & LIBRARY SERVICES: “Piloting CT-LASER+”: Piloting an Online Collaborative Network for Integrating Computational Thinking into Library and Archival (Science) Education (Research) & Practice

A $300K IMLS-funded project. The University of Maryland iSchool is piloting an online national collaborative network of educators and practitioners to enable the sharing and dissemination of computational case studies and lesson plans through an open source, cloud-based interactive platform based on Jupyter Notebooks. Jupyter is self-described as a free, open-source, interactive web tool known as a computational notebook, which researchers can use to combine software code, computational output, explanatory text and multimedia resources in a single document. This project focuses on participants who have a master’s-level education in order to target the professional development of future practitioners across the nation. The large group of collaborators will support faculty and students through a supportive community of teachers and practitioners, dedicated to modernizing archival and library education. The ultimate goal is to contribute to the development of faculty and library digital leaders.

● FDR PRESIDENTIAL LIBRARY & MUSEUM: “ML-FDR”: Machine Learning (ML) Machine Learning Strategies for FDR Presidential Library & Museum Collections — towards metadata extraction [See: CLIR/AERI July 6, 2020: https://ai-collaboratory.net/projects/ml-fdr/

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